To this end, our model leverages the GANformer architecture we introduced earlier this year [1]} – a bipartite transformer that uses soft-attention to cooperetaviely translate a set of latents into a generated image [2]}, [1]}. In GANformer2, we take this idea a step further, and show how we can use the bipartite transformer as a backbone for a more structured process, that enables not only an implicit, but also an explicit modeling of the objects and constituents composing the image's scene. Our iterative design endows the GANformer2 model with the flexibility to consider the correlations and interactions not only among latents but between synthesized scene elements directly, circumventing the need for partial independence assumptions made by earlier works [4]}, [5]}, [6]}, and thereby managing to scale to natural real-world scenes. Concurrently, the explicit object-oriented structure enhances the model's compositionallity so to outperform past coarse-to-fine cascade [7]}, layered [8]}, and hierarchical approaches [9]}, which, like us, employ a two-stage layout-based procedure, but contrary to our work, do so in a non-compositional fashion, and indeed were mostly explored within limited domains, such as indoor NYUv2 [10]}, faces [11]}, pizzas [12]}, or bird photographs [13]}.
